It is not clear to us why the authors of [1]} presented their results separately for each subject, when in [2]} the experiments are performed in a multi-subject pooled analysis.
In Sect. REF  we already showed how the per-subject analysis may enhance the effect of temporal correlation on classification results. The per-subject analysis is critical mainly because EEG data are known to be highly replicable within a person , but also highly specific from person to person , . In fact, the difference in EEG activity is so great from person to person that it has even been proposed as a method for hard-to-mimic and impossible-to-steal biometric identification, and it is able to identify individuals with  95 to 100 percent accuracy . This variability can be observed in the results reported in [1]}. For instance, in the block-design experiments (Table 4 in [1]}, and Tables 21-25 in [1]}'s appendix), classification performance of the 1D-CNN method varies from 37.80% to 70.50%. Similarly, in the rapid-design experiment with block-level labels (Table 9 in [1]}, and Tables 41-45 in [1]}'s appendix), classification accuracy varies from 19.20% to 84.10%. Thus, classification performance was not consistent from subject to subject.
To further elucidate that the per-subject analysis is problematic, we compare the test performance of the 1D-CNN in two settings: 1) the model is trained and tested using single-subject data; 2) the model is trained with data pooled from all subjects and then tested on single subject data. The results of this comparison are given in Table REF  and clearly show how pooling data accounts for inter-subject variability by making classifiers less influenced by subject-specific representation. More specifically, Table REF  shows that the variability (measured in terms of standard deviation) among per-subject results decreases significantly when a classifier is trained using all subjects' data (on the rapid design with block labels, the standard deviation of performance between subjects drops from 19.0% in [1]} to 3.6% in our case). Furthermore, this allows the model to focus on inter-subject discriminative features, reducing the bias due to possible temporal correlation in single subject neural responses.
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